Abstract
The lipid oxidation of fresh chilled pork was evaluated rapidly and contactlessly by near-infrared hyperspectral imaging during different storage periods. The Gaussian filter smoothing (GFS), moving average smoothing (MAS), Savitzky-Golay convolution smoothing (SGCS), median filtering smoothing (MFS), multiplicative scatter correction (MSC), standard normal variable (SNV) correction and baseline correction (BC) were used to preprocess the reflectance spectra of the pork samples at 900~1 700 nm. After the seven kinds of pretreatments mentioned above, Partial least squares regression (PLSR) model was established to explore the quantitative relationship between spectral data and the 2-thiobarbituric acid (TBA) value. As a result, the GFS-PLSR model based on full 486 wavelengths of GFS spectra showed better performance in prediction (RP=0.919, RMSEP=0.036 mg/100 g). Regression coefficients (RC) method, stepwise and successive projections algorithm (SPA) were used to select optimal wavelengths to simplify the GFS-PLSR model and improve the efficiency of prediction. It was indicated that the RC-GFS-PLSR model established with the 29 optimal wavelengths selected from GFS spectra by RC showed better performance in prediction (RP=0.924, RMSEP=0.034 mg/100 g), similar to GFS-PLSR model. The overall results showed that the TBA value could be quantitatively predicted by using near-infrared hyperspectral imaging technology combined with RC method, and this could be applied to realize the rapid and contactless evaluation of lipid oxidation in pork.
Publication Date
2-18-2023
First Page
117
Last Page
122
DOI
10.13652/j.issn.1003-5788.2020.08.021
Recommended Citation
Hong-ju, HE; Wei, WANG; Bo, LI; Yu-ling, WANG; Ya-dong, ZHU; Sheng-qi, JIANG; Han-jun, MA; and Fu-sheng, CHEN
(2023)
"Rapid and non-contact evaluation of lipid oxidation in fresh chilled pork by near-infrared hyperspectral imaging,"
Food and Machinery: Vol. 36:
Iss.
8, Article 21.
DOI: 10.13652/j.issn.1003-5788.2020.08.021
Available at:
https://www.ifoodmm.cn/journal/vol36/iss8/21
References
[1] 张雷蕾,李永玉,彭彦昆,等.基于高光谱成像技术的猪肉新鲜度评价[J].农业工程学报,2012,28(7):254-259.
[2] 张玉斌,邰晶晶,刘金鑫,等.天然抗氧化剂对冷却藏羊肉贮藏过程中脂质氧化影响的研究[J].肉类工业,2017(12):13-18.
[3] 刘峥,殷勇.基于高光谱技术的香肠亚硝酸盐快速检测方法[J].食品与机械,2019,35(5):78-82.
[4] 王光辉,殷勇.基于高光谱融合神经网络的玉米黄曲霉毒素B1和赤霉烯酮含量预测[J].食品与机械,2018,34(11):64-69.
[5] 谢安国,康怀彬,王飞翔,等.高光谱成像检测煎制中调理牛肉品质的变化[J].食品与机械,2018,34(11):20-23,54.
[6] XIONG Zheng-jie,SUN Da-wen,PU Hong-bin,et al.Non-destructive prediction of thiobarbituricacid reactive substances(TBARS)value for freshness evaluation of chicken meat using hyperspectral imaging[J].Food Chemistry,2015,179:175-181.
[7] WU Xiang,SONG Xing-lin,QIU Zheng-jun,et al.Mapping of TBARS distribution in frozen-thawed pork using NIR hyperspectral imaging[J].Meat Science,2016,113:92-96.
[8] 王慧,何鸿举,张海曼,等.高光谱成像技术快速预测冷鲜鸡胸肉的嫩度[J].海南师范大学学报(自然科学版),2018,31(2):164-170.
[9] HE Hong-ju,SUN Da-wen.Selection of informative spectral wavelength for evaluating and visualising enterobacteriaceae contamination of salmon flesh[J].Food Analytical Methods,2015,8(10):2 427-2 436.
[10] 郭中华,郑彩英,金灵.基于近红外高光谱成像的冷鲜羊肉表面细菌总数检测[J].食品工业科技,2014,35(20):66-68.
[11] 毛莎莎,曾明,何绍兰,等.哈姆林甜橙果实内在品质的可见—近红外漫反射光谱无损检测法[J].食品科学,2010,31(14):258-263.
[12] DAI Qiong,SUN Da-wen,XIONG Zheng-jie,et al.Recent advances in data mining techniques and their applications in hyperspectral image processing for the food industry[J].Comprehensive Reviews in Food Science and Food Safety,2014,13(5):891-905.
[13] 赵高长,张磊,武风波.改进的中值滤波算法在图像去噪中的应用[J].应用光学,2011,32(4):678-682.
[14] JIA Bei-bei,YOON S C,ZHUANG Hong,et al.Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging[J].Journal of Food Engineering,2017,208:57-65.
[15] CHENG Wei-wei,SUN Da-wen,CHENG Jun-hu.Pork biogenic amine index(BAI)determination based on chemometric analysis of hyperspectral imaging data[J].LWT-Food Science and Technology,2016,73:13-19.
[16] 潘冉冉,骆一凡,王昌,等.高光谱成像的油菜和杂草分类方法[J].光谱学与光谱分析,2017,37(11):3 567-3 572.
[17] 何鸿举,王玉玲,乔红,等.基于长波近红外光谱快速无接触评估小麦籽粒含水率[J].海南师范大学学报(自然科学版),2019,32(1):26-32.
[18] HE Hong-ju,WU Di,SUN Da-wen.Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets[J].Journal of Food Engineering,2014,126:156-164.
[19] PENG Yan-kun,TAO Fei-fei,LI Yong-yu,et al.Rapid detection of total viable count of chilled pork using hyperspectral scattering technique[J].Proc of Spie,2010,7 676:76760K1-76760K8.
[20] HE Hong-ju,SUN Da-wen,WU Di.Rapid and real-time prediction of lactic acid bacteria(LAB)in farmed salmon flesh using near-infrared(NIR)hyperspectral imaging combined with chemometric analysis[J].Food Research International,2014,62:476-483.
[21] ElMASRY G,SUN Da-wen,PAUL A.Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging[J].Food Research International,2011,44(9):2 624-2 633.
[22] FENG Yao-ze,SUN Da-wen.Determination of total viable count(TVC)in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms[J].Talanta,2013,105:244-249.
[23] HE Hong-ju,SUN Da-wen.Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging[J].LWT-Food Science and Technology,2015,62(1):11-18.
[24] 何鸿举,王玉玲,乔红,等.NIR光谱法快速预测小麦籽粒干物质含量[J].海南师范大学学报(自然科学版),2019,32(1):36-41.
[25] 刘欣,马金爽,张晓青,等.基于近红外技术快速检测青金桔果粉中β-胡萝卜素含量[J].海南师范大学学报(自然科学版),2018,31(3):31-36.